Vegetation structure data are essential for understanding the functioning of terrestrial ecosystems and for informing various science-policy interfaces. Recent years have seen a growing demand for high-resolution data on vegetation structure, driving the prediction of such metrics at fine resolutions (1 m - 30 m) at state, continental, and global scales by combining satellite data with machine learning. As these initiatives expand, it is crucial for the remote sensing and ecological communities to actively discuss the quality and usability of these products. Here, we (i) provide a brief overview of space-borne lidar missions measuring vegetation structure; (ii) using global canopy height models (CHMs) as an example, we demonstrate that predicted products exhibit significant errors exceeding natural changes in canopy height observed over a 10-year period, indicating that even a 10-year-old CHM derived from airborne laser scanning (ALS) is superior to currently available predicted CHMs; therefore, (iii) we recommend that regions with abundant ALS data prioritize harmonizing ALS-based vegetation metrics rather than relying solely on much less accurate predicted products derived from satellite data. We investigated the availability of ALS data in Europe and found that they are available for 26 countries, collected mostly between 2009 and 2024. We argue that, despite variations in data characteristics, including temporal inconsistencies and differences in point density and classification accuracy, the production of vegetation structure metrics, particularly CHMs, in raster format at fine resolution is both necessary and feasible. As new acquisitions are planned or underway, it is important to coordinate efforts to facilitate harmonization, develop continent-wide products, and ensure free access for research and policy communities. Beyond numerous ecological applications, such consistent benchmark datasets are crucial for calibrating future Earth Observation missions, making them essential for producing truly global, fine-resolution vegetation structure data.
Abstract. The shape and area of the crown of each tree are among the most influential parameters for identifying and controlling the processes of photosynthesis, respiration, transpiration and its management. In such a way that various physiographic functions, such as carbon dioxide absorption, light energy absorption, oxygen release and transpiration, which are vital for the growth and development of the tree, are done in the crown. In this research, the RGB image of the UAV with a spatial resolution of 2 cm was resampled to three pixel sizes of 10, 30 and 50 cm. Then, each image was classified separately by SVM, ANN and MLC algorithms, which are all part of Ensemble. In the next step, each of the obtained crowns was compared with the digitization of the same crown, and based on the area of the crown obtained from each classification and normalization method, the weight was obtained specifically for the same crown. Finally, by using the weighted majority voting method, classifications were fusioned at the decision level. The results showed that the ANN method gives better results in all pixel sizes compared to MLC and SVM. Also, the combination of different classification methods with the weighted majority voting method based on the weight assigned to the same crown based on each classification method has significantly increased the classification accuracy of the tree crown in all the sizes of the analyzed pixels.
Explaining the high diversity of tree species in tropical forests remains a persistent challenge in ecology. The analysis of spatial patterns of different species and their spatial diversity captures the spatial variation of species behaviors from a ‘plant’s eye view’ of a forest community. To measure scale-dependent species-species interactions and species diversity at neighborhood scales, we applied uni- and bivariate pair correlation functions and individual species area relationships (ISARs) to two fully mapped 2-ha plots of tropical evergreen forests in north-central Vietnam. The results showed that (1) positive conspecific interactions dominated at scales smaller than 30 m in both plots, while weak negative interactions were only observed in P2 at scales larger than 30 m; (2) low numbers of non-neutral interactions between tree species were observed in both study plots. The effect of scale separation by habitat variability on heterospecific association was observed at scales up to 30 m; (3) the dominance of diversity accumulators, the species with more diversity in local neighborhoods than expected by the null model, occurred at small scales, while diversity repellers, the species with less diversity in local neighborhoods, were more frequent on larger scales. Overall, the significant heterospecific interactions revealed by our study were common in highly diverse tropical forests. Conspecific distribution patterns were mainly regulated by topographic variation at local neighborhood scales within 30 m. Moreover, ISARs were also affected by habitat segregation and species diversity patterns occurring at small neighborhood scales. Mixed effects of limited dispersal, functional equivalence, and habitat variability could drive spatial patterns of tree species in this study. For further studies, the effects of topographical variables on tree species associations and their spatial autocorrelations with forest stand properties should be considered for a comprehensive assessment.
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